NVIDIA NCA-GENL Sample Questions With Full Explanations
Here is a set of 8-10 multiple-choice questions designed to match the difficulty and domain coverage of the NVIDIA NCA-GenL exam. These questions focus on practical application, architecture, and best practices for generative AI.
Intro: This practice set covers key areas of the NVIDIA Certified Associate - Generative AI (NCA-GenL) exam, including foundational concepts, NVIDIA AI stack (NeMo, TensorRT-LLM), model optimization, and deployment strategies. Test your readiness by tackling these scenario-based questions. We cover this in depth in AI-900 Sample Questions With Full Explanations.
Before diving in, use the free NVIDIA-Certified Associate: Generative AI and LLMs readiness check to spot which topics need more review.
Q1. A data scientist is fine-tuning a large language model for legal document summarization. They have a limited dataset of 5,000 high-quality legal summaries. Which approach is the MOST effective to achieve high accuracy while minimizing the risk of catastrophic forgetting?
A. Full fine-tuning of all model parameters
B. Prompt engineering with zero-shot inference
C. Parameter-Efficient Fine-Tuning (PEFT) using LoRA
D. Pre-training a new model from scratch on legal corpora
Answer: C
Explanation: PEFT (like LoRA) is ideal for adapting a large model to a specific domain with limited data. It freezes the original weights and trains a small set of adapters, which is faster, requires less memory, and significantly reduces the risk of catastrophic forgetting compared to full fine-tuning. Options A and D are resource-intensive and prone to overfitting with small data, while B is unlikely to achieve the specialized quality needed.
Q2. An inference engineer is deploying a generative model and needs to reduce the memory footprint by 4x without retraining. Which NVIDIA technology is specifically designed for this purpose?
A. TensorRT-LLM with FP16 precision
B. TensorRT-LLM with INT4 AWQ quantization
C. NVIDIA NeMo Framework for distributed training
D. NVIDIA Triton Inference Server with dynamic batching
Answer: B
Explanation: INT4 quantization (e.g., AWQ or GPTQ) reduces model weights from 16-bit to 4-bit, achieving an approximate 4x reduction in memory footprint. While TensorRT-LLM supports FP16 (a 2x reduction), the 4x target requires 4-bit precision. NeMo is for training, and Triton handles serving orchestration, not weight compression.
Q3. A developer is building a RAG pipeline for a customer support chatbot. They need to retrieve the most relevant documents from a vector database. What is the correct sequence of steps for the retrieval process?
A. Generate embeddings for the query → Encode documents → Calculate cosine similarity → Return top-k
B. Encode documents → Generate embeddings for the query → Calculate cosine similarity → Return top-k
C. Calculate cosine similarity → Encode documents → Generate embeddings for the query → Return top-k
D. Generate embeddings for the query → Calculate cosine similarity → Encode documents → Return top-k
Answer: B
Explanation: In a RAG pipeline, the corpus of documents must be pre-processed and encoded into a vector database before a query arrives. At runtime, the query is embedded, and its vector is compared against the pre-computed document embeddings using similarity metrics like cosine similarity to return the top-k most relevant chunks.
Q4. A team is evaluating different model sizes for an edge deployment. They have a target latency of 50ms and a memory limit of 8GB. Which comparison metric is MOST critical for selecting the right model variant?
A. Training time on a single A100 GPU
B. Number of parameters in the model We recommend pairing this with AWS AI Practitioner Sample Questions With Full Explanations.
C. Tokens per second (inference throughput) and VRAM footprint
D. Perplexity score on the training dataset
Answer: C
Explanation: For edge deployment, the primary constraints are inference speed (latency) and memory (VRAM). Tokens per second directly measures throughput against the 50ms latency target, and VRAM footprint checks memory compatibility. Parameter count is a rough proxy, but actual optimized inference metrics are the definitive measure for deployment.
Q5. A developer is using the NVIDIA NeMo framework to train a custom T5 model. They want to leverage multiple GPUs to speed up the process. What is the standard way to configure and launch this distributed training job?
A. Manually assign each GPU layer to a different process using Python threads.
B. Use NeMo's PyTorch Lightning integration and set the trainer.devices and trainer.strategy parameters.
C. Write a custom MPI script to handle the gradient synchronization manually.
D. Use the torch.distributed.launch command without modifying the NeMo configuration.
Answer: B
Explanation: NeMo is built on PyTorch Lightning, which provides a high-level abstraction for distributed training. By setting trainer.devices (e.g., 8) and trainer.strategy (e.g., "ddp"), NeMo handles the data parallelism, gradient synchronization, and checkpointing automatically. Options A and C are low-level and error-prone, while D ignores the NeMo-specific configuration needed.
Q6. During inference with TensorRT-LLM, a developer notices that the GPU utilization is low for a single request. To maximize throughput for a high-volume API, which feature should they enable?
A. In-flight Batching (Continuous Batching)
B. KV Cache quantization
C. FP16 precision mode
D. LoRA adapter switching
Answer: A
Explanation: In-flight batching allows the server to dynamically add new requests to the execution queue as soon as a previous sequence completes, rather than waiting for a static batch to finish. This keeps the GPU busy and maximizes utilization and throughput for concurrent requests. The other options improve memory efficiency or flexibility, not raw GPU utilization for multiple requests.
Q7. A developer is creating a custom generative AI model and needs to train it on a custom dataset. Which NVIDIA tool is the MOST appropriate for managing the end-to-end lifecycle, including data curation, training, and evaluation?
A. NVIDIA TensorRT
B. NVIDIA NeMo
C. NVIDIA cuDNN
D. NVIDIA DALI
Answer: B Looking for more detail? See AWS Data Engineer Associate Sample Questions With Full Explanations.
Explanation: NVIDIA NeMo is an end-to-end framework specifically for building, customizing, and deploying generative AI models. It includes data curation tools (NeMo Curator), training scripts, and evaluation harnesses. TensorRT is for inference optimization, cuDNN is a low-level acceleration library, and DALI focuses on data loading and augmentation.
Q8. A model is generating biased text that reflects gender stereotypes. According to best practices for responsible AI, what is the FIRST step a developer should take to address this?
A. Immediately apply a post-processing filter to remove biased words.
B. Retrain the model on a larger, uncurated dataset.
C. Audit the training data and model outputs for bias using a structured framework.
D. Switch to a larger model with more parameters.
Answer: C
Explanation: Responsible AI practices dictate that bias must be identified and measured before it can be mitigated. The first step is to audit the data (for representation) and the model outputs (for harmful patterns) using frameworks like AI Fairness 360 or NeMo Guardrails. Blindly applying filters (A) or retraining (B, D) without understanding the root cause is ineffective and can lead to unintended consequences.
Q9. A team is serving multiple fine-tuned models (e.g., different LoRA adapters for different clients) on a single GPU. What is the MOST efficient NVIDIA solution to serve these models concurrently?
A. Deploy each model on a separate virtual machine.
B. Use NVIDIA Triton with LoRA adapter support and dynamic batching.
C. Merge all LoRA adapters into a single, large model.
D. Use TensorRT-LLM to compile each model separately and switch between them manually.
Answer: B
Explanation: NVIDIA Triton Inference Server supports serving multiple LoRA adapters on a single base model. This allows a single GPU to host many fine-tuned models efficiently, switching adapters per request without loading a new model graph. This is far more memory-efficient than separate VMs (A) or merging adapters (C), which is not always possible and defeats the purpose of PEFT.
Q10. A developer is using NeMo Guardrails to manage a conversational AI system. They want to ensure the model does not respond to prompts asking for illegal advice. Which type of guardrail is the MOST appropriate to implement?
A. A topical rail to keep the conversation on-topic.
B. A fact-checking rail to verify the model's claims.
C. A safety rail with a user intent definition for "illegal activities".
D. A hallucination detection rail to prevent made-up answers.
Answer: C
Explanation: Safety rails are designed to define and block specific user intents or topics that are considered harmful or inappropriate, such as illegal activities. Topical rails keep the conversation on a general subject, while fact-checking and hallucination rails address accuracy, not safety policy violations. The safety rail would trigger on the user's intent and prevent the LLM from generating a harmful response.
Closing: These questions represent the analytical and technical depth required for the NCA-GenL certification. Success depends not just on memorizing terms, but on understanding how the NVIDIA AI stack integrates to solve real-world generative AI challenges.
To prepare for that depth, shape a schedule that fits your available study time with the free NVIDIA-Certified Associate: Generative AI and LLMs study plan.
Identify your weak spots early — work through NVIDIA-Certified Associate: Generative AI and LLMs practice papers while you still have time to review.